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Hazard Recognition Patterns Demonstrated by Construction Workers

Author

Listed:
  • S M Jamil Uddin

    (Department of Civil, Construction, and Environmental Engineering, North Carolina State University, 2501 Stinson Dr., Raleigh, NC 27607, USA)

  • Alex Albert

    (Department of Civil, Construction, and Environmental Engineering, North Carolina State University, 2501 Stinson Dr., Raleigh, NC 27607, USA)

  • Abdullah Alsharef

    (Department of Civil, Construction, and Environmental Engineering, North Carolina State University, 2501 Stinson Dr., Raleigh, NC 27607, USA)

  • Bhavana Pandit

    (Department of Civil, Construction, and Environmental Engineering, North Carolina State University, 2501 Stinson Dr., Raleigh, NC 27607, USA)

  • Yashwardhan Patil

    (Department of Civil, Construction, and Environmental Engineering, North Carolina State University, 2501 Stinson Dr., Raleigh, NC 27607, USA)

  • Chukwuma Nnaji

    (Department of Civil, Construction, and Environmental Engineering, The University of Alabama, 3023 HM Comer, Tuscaloosa, AL 35487, USA)

Abstract

Construction workers fail to recognize a large number of safety hazards. These unrecognized safety hazards can lead to unintended hazard exposure and tragic safety incidents. Unfortunately, traditional hazard recognition interventions (e.g., job hazard analyses and safety training) have been unable to tackle the industry-wide problem of poor hazard recognition levels. In fact, emerging evidence has demonstrated that traditional hazard recognition interventions have been designed without a proper understanding of the challenges workers experience during hazard recognition efforts. Interventions and industry-wide efforts designed based on a more thorough understanding of these challenges can yield substantial benefits—including superior hazard recognition levels and lower injury rates. Towards achieving this goal, the current investigation focused on identifying hazard categories that workers are more proficient in recognizing and others that they are less proficient in recognizing (i.e., hazard recognition patterns). For the purpose of the current study, hazards were classified on the basis of the energy source per Haddon’s energy release theory (e.g., gravity, motion, electrical, chemical, etc.). As part of the study, 287 workers representing 57 construction workplaces in the United States were engaged in a hazard recognition activity. Apart from confirming previous research findings that workers fail to recognize a disproportionate number of safety hazards, the results demonstrate that the workers are more proficient in recognizing certain hazard types. More specifically, the workers on average recognized roughly 47% of the safety hazards in the gravity, electrical, motion, and temperature hazard categories while only recognizing less than 10% of the hazards in the pressure, chemical, and radiation hazard categories. These findings can inform the development of more robust interventions and industry-wide initiatives to tackle the issue of poor hazard recognition levels in the construction industry.

Suggested Citation

  • S M Jamil Uddin & Alex Albert & Abdullah Alsharef & Bhavana Pandit & Yashwardhan Patil & Chukwuma Nnaji, 2020. "Hazard Recognition Patterns Demonstrated by Construction Workers," IJERPH, MDPI, vol. 17(21), pages 1-14, October.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:21:p:7788-:d:434130
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    References listed on IDEAS

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    Cited by:

    1. S M Jamil Uddin & Alex Albert & Anto Ovid & Abdullah Alsharef, 2023. "Leveraging ChatGPT to Aid Construction Hazard Recognition and Support Safety Education and Training," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    2. Mohanad Kamil Buniya & Idris Othman & Serdar Durdyev & Riza Yosia Sunindijo & Syuhaida Ismail & Ahmed Farouk Kineber, 2021. "Safety Program Elements in the Construction Industry: The Case of Iraq," IJERPH, MDPI, vol. 18(2), pages 1-13, January.
    3. Akeem Pedro & Anh-Tuan Pham-Hang & Phong Thanh Nguyen & Hai Chien Pham, 2022. "Data-Driven Construction Safety Information Sharing System Based on Linked Data, Ontologies, and Knowledge Graph Technologies," IJERPH, MDPI, vol. 19(2), pages 1-18, January.
    4. Aminu Darda’u Rafindadi & Nasir Shafiq & Idris Othman & Miljan Mikić, 2023. "Mechanism Models of the Conventional and Advanced Methods of Construction Safety Training. Is the Traditional Method of Safety Training Sufficient?," IJERPH, MDPI, vol. 20(2), pages 1-19, January.
    5. Aminu Darda’u Rafindadi & Nasir Shafiq & Idris Othman, 2022. "A Conceptual Framework for BIM Process Flow to Mitigate the Causes of Fall-Related Accidents at the Design Stage," Sustainability, MDPI, vol. 14(20), pages 1-37, October.

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